Structural Inequality in D.C. Civil Service, 1987–2023¶
Rosie/Yutong Meng
May 15, 2025 | University of Colorado Denver, Spring Semester 2025
(Generative AI was used as a coding partner to generate the code)
Introduction¶
Amid a surge of high-profile layoffs dominating headlines—from tech giants to government contractors—questions about job security, workplace inequality, and institutional mobility are more relevant than ever. In a recent AP News article in 2025, a former federal worker reflects on over a decade of public service, only to realize that despite years of dedication, upward mobility within the system remained elusive (Kinnard, 2025). Her story—emerging amid mass layoffs at a tech company—reveals something deeper: that structural stagnation and invisible hierarchies are not exclusive to the private sector. In fact, they are embedded in the design of public employment.
This observation resonates with growing concerns over inequality within government institutions. While public employment is often perceived as stable and meritocratic, closer examination reveals significant disparities—across appointment types, job classifications, and gendered roles. Through a data analysis of the DC public employee salary system, this research analysis shows how bureaucratic structures bring inequality, even in spaces assumed to be neutral and equitable.
The dataset used in this study, “DC Public Employee Salary,” is sourced from the U.S. federal government’s open data platform, Data.gov, and is publicly released by the Government of the District of Columbia. This dataset contains 118,604 records spanning from July 30, 1966 to April 1, 2025. The database includes key information such as employee name, job title, department (DESCRSHORT), salary (COMPRATE), employment type (GVT_TYPE_OF_APPT), job grade, and hire date. The dataset provides a rare long-term view of the DC public workforce across nearly six decades, enabling both historical and structural analysis. Here is the sample of the dataset below:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.io as pio
import plotly.offline as pyo
pio.renderers.default = "jupyterlab"
pyo.init_notebook_mode(connected=True)
df = pd.read_csv("DC_Public_Employee_Salary.csv")
df['JOBTITLE'] = df['JOBTITLE'].str.title()
df['hire_date'] = pd.to_datetime(df['HIREDATE_STRING'], errors='coerce')
df['hire_year'] = df['hire_date'].dt.year
df['Name'] = df['FIRST_NAME'].str[0] + '. ' + df['LAST_NAME'].str[0] + '.'
cols_to_display = ['Name', 'JOBTITLE', 'DESCRSHORT', 'GRADE', 'COMPRATE', 'hire_date', 'GVT_TYPE_OF_APPT']
sample_df = df[cols_to_display].head(5)
sample_df.style.set_table_attributes('border="1" cellpadding="4" cellspacing="0"')
| Name | JOBTITLE | DESCRSHORT | GRADE | COMPRATE | hire_date | GVT_TYPE_OF_APPT | |
|---|---|---|---|---|---|---|---|
| 0 | S. P. | Facilities Systems Specialist | DBH | 12 | 90805.000000 | 1987-10-01 00:00:00 | CS - Reg |
| 1 | P. O. | Nurse | DBH | 05 | 76510.000000 | 2000-02-14 00:00:00 | CS - Reg |
| 2 | D. F. | Special Projects Coordinator | DCG | 12 | 90805.000000 | 2020-03-16 00:00:00 | CS - Term |
| 3 | S. K. | Behavioral Health Technician | DBH | 08 | 70600.000000 | 1988-06-07 00:00:00 | CS - Reg |
| 4 | O. F. | Behavioral Health Technician | DBH | 08 | 70600.000000 | 1995-09-18 00:00:00 | CS - Reg |
To access the full dataset, visit DC Public Employee Salary.
Methodology¶
This study uses the “DC Public Employee Salary” dataset, which is available on the U.S. federal government’s open data platform, Data.gov, and published by the Government of the District of Columbia. The dataset contains 118,604 records from July 30, 1966, to April 1, 2025, covering nearly sixty years of salary information for public sector employees in Washington, D.C.
The data was collected by various departments and agencies within the D.C. government as part of its human resources management and commitment to public transparency. The primary purpose of collecting this data is to enhance transparency and accountability in government operations, allowing the public to access and analyze salary structures and employment patterns among government employees.
Due to the dataset’s nearly six-decade timespan and the occurrence of multiple administrative reforms over the years, there are inconsistencies in data recording formats, job title conventions, and salary calculation methods. For example, job titles in certain years appear entirely in uppercase, and some employment date entries could not be converted into valid date formats. It is important to note that the dataset does not include personal demographic information such as employee gender, race, or educational background, which limits the scope of analysis regarding structural inequality.
To conduct effective analysis, I first performed data preprocessing using Python (with libraries such as Pandas and Matplotlib). This included standardizing job title formats, converting employment date fields, extracting hire year variables, and removing records with missing or zero salary values. Because gender data was not included in the dataset, I referred to existing research on gendered occupational coding to classify positions such as “Teacher” and “Nurse” as female-coded jobs, and “Police Officer” and “Engineer” as male-coded jobs. Based on this, a new variable—“gender coding”—was created to analyze the relationship between gender structure and salary differences. It should be noted, however, that some positions involve multiple responsibilities (e.g., “Program Analyst”), which may not fit neatly into a single category. This introduces potential ambiguity into the classification criteria.
To better visualize salary disparities across different groups, I produced several charts, including a salary trend line by hire year, departmental salary growth curves, bar charts of salary by job title, and boxplots comparing salaries across gender-coded categories. The following sections present the main components of the analysis.
Analysis and Findings¶
To begin understanding structural trends within the D.C. public workforce, we first examine how average annual salaries have changed over time. The chart below illustrates the average salary of public employees by hire year, from 1987 to 2023.
yearly_salary = df.groupby('hire_year')['COMPRATE'].mean().reset_index()
plt.figure(figsize=(16,8))
plt.plot(yearly_salary['hire_year'], yearly_salary['COMPRATE'], marker='o', color='navy')
plt.xlabel('Hire Year')
plt.ylabel('Average Annual Salary')
plt.title('Average Salary of DC Public Employees (1987–2023)', fontsize=16)
plt.grid(True)
plt.axvspan(1987, 2000, color='lightblue', alpha=0.3, label='Moderate Growth (1987–2000)')
plt.axvspan(2000, 2008, color='lightgreen', alpha=0.3, label='Rapid Growth (2000–2008)')
plt.axvspan(2008, 2010, color='lightcoral', alpha=0.3, label='Financial Crisis (2008–2010)')
plt.axvspan(2010, 2018, color='khaki', alpha=0.3, label='Slow Recovery (2010–2018)')
plt.axvspan(2018, 2023, color='plum', alpha=0.3, label='Pandemic and Sharp Decline (2018–2023)')
important_years = {
2008: 'Financial Crisis',
2020: 'COVID-19',
2022: 'Sharp Decline'
}
for year, label in important_years.items():
if year in yearly_salary['hire_year'].values:
salary = yearly_salary[yearly_salary['hire_year'] == year]['COMPRATE'].values[0]
plt.annotate(label,
xy=(year, salary),
xytext=(year+1, salary+3000),
arrowprops=dict(facecolor='black', arrowstyle='->'),
fontsize=10,
ha='left')
plt.legend(loc='lower right', fontsize=10)
plt.tight_layout()
plt.show()
The first chart visualizes the average salary of D.C. public employees from 1987 to 2023. At a glance, it reveals a general upward trend from the late 1980s through the mid-2000s, followed by stagnation and then a sharp decline in recent years. This reflects the salary changes of the newly recruited employees.
While these patterns may initially appear to mirror broader economic cycles, a closer look suggests deeper structural shifts within the civil service system. The early period of growth likely reflects a phase of government expansion and professionalization, whereas the post-2008 flattening may correspond to budget tightening and hiring freezes following the financial crisis. The significant decline beginning in 2018, which accelerates during the COVID-19 pandemic, may be linked not only to fiscal austerity but also to changes in workforce composition—such as an increase in temporary appointments and a loss of higher-paid veteran employees. These possibilities point to systemic transformations in how public labor is valued and structured. In the sections that follow, we will explore these dynamics in greater detail by disaggregating salary trends across job titles, departments, appointment types, and gender-coded roles.
It is important to note, however, that the dataset does not explicitly indicate whether the reported salary reflects the employee’s pay at the time of hire or their current salary as of the data collection year. Given this ambiguity, the observed decline in average salary among more recent hires may not necessarily signal a broader downward trend in government compensation. Instead, it could reflect the natural career progression—individuals hired in recent years may simply be starting at lower pay grades due to having less tenure.
The following chart, which illustrates the salary distribution between “CS – Reg” (regular employees) and “CS – Term” (temporary employees), reveals the deeper structural divide of “equal work, unequal rights.”
df = pd.read_csv("DC_Public_Employee_Salary.csv")
df_filtered = df[
(df['COMPRATE'].notnull()) & (df['COMPRATE'] > 0) &
(df['GVT_TYPE_OF_APPT'].isin(['CS - Reg', 'CS - Term']))
]
plt.figure(figsize=(12, 6))
sns.histplot(
data=df_filtered,
x='COMPRATE',
hue='GVT_TYPE_OF_APPT',
bins=50,
kde=True,
element='step'
)
plt.xlabel('Annual Salary')
plt.title('Salary Distribution: CS - Reg vs CS - Term')
plt.grid(True)
plt.tight_layout()
plt.show()
avg_salary_by_type = df_filtered.groupby('GVT_TYPE_OF_APPT')['COMPRATE'].mean()
print(avg_salary_by_type)
reg_salaries = df_filtered[df_filtered['GVT_TYPE_OF_APPT'] == 'CS - Reg']['COMPRATE']
term_salaries = df_filtered[df_filtered['GVT_TYPE_OF_APPT'] == 'CS - Term']['COMPRATE']
t_stat, p_value = ttest_ind(reg_salaries, term_salaries, equal_var=False)
print(f"t-statistic: {t_stat:.3f}, p-value: {p_value:.4f}")
GVT_TYPE_OF_APPT CS - Reg 88533.964605 CS - Term 75468.564334 Name: COMPRATE, dtype: float64
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[19], line 26 23 reg_salaries = df_filtered[df_filtered['GVT_TYPE_OF_APPT'] == 'CS - Reg']['COMPRATE'] 24 term_salaries = df_filtered[df_filtered['GVT_TYPE_OF_APPT'] == 'CS - Term']['COMPRATE'] ---> 26 t_stat, p_value = ttest_ind(reg_salaries, term_salaries, equal_var=False) 27 print(f"t-statistic: {t_stat:.3f}, p-value: {p_value:.4f}") NameError: name 'ttest_ind' is not defined
Although both “CS - Reg” (regular employees) and “CS - Term” (temporary employees) are part of the D.C. government workforce, the chart clearly reveals a significant disparity in their salary distributions. CS - Reg positions exhibit a wider salary spread, a higher peak, and a notably higher average salary compared to CS - Term positions. Employment type not only defines the terms of the labor contract, but also shapes access to promotion pathways, salary grades, and institutional benefits. CS - Reg employees typically enjoy long-term tenure, pension plans, and performance-based advancement, whereas CS - Term positions are often used for temporary coverage or project-based contracts, lacking sustained investment in compensation.
The government maintains this institutionalized dual-track system ostensibly for fiscal flexibility—allowing vacancies to be filled at lower cost. However, it also creates a built-in hierarchy: who gets to be employed long-term and who remains in a temporary, replaceable role. This mechanism means that many term employees, even after working within the system for years, are never granted equal opportunities for advancement—as if they’re permanently stuck on the “bench.” For individuals, this directly impacts their ability to achieve long-term economic security and career progression; for the system, it silently reinforces structural inequality from within.
To explore the salary differences among specific positions specifically, let's take a look at average salaries across the ten most common job titles in the D.C. public workforce.
import plotly.express as px
top_jobs = df['JOBTITLE'].value_counts().head(10).reset_index()
top_jobs.columns = ['Job Title', 'Count']
fig = px.pie(
top_jobs,
values='Count',
names='Job Title',
title='Top 10 Most Common Job Titles in DC Public Sector',
color_discrete_sequence=px.colors.qualitative.Pastel
)
fig.update_traces(
textinfo='percent+label',
hoverinfo='skip',
hovertemplate=None
)
fig.show()
top_jobs = df['JOBTITLE'].value_counts().head(10).index.tolist()
df_top_jobs = df[df['JOBTITLE'].isin(top_jobs)]
avg_salary_by_job = df_top_jobs.groupby('JOBTITLE')['COMPRATE'].mean().sort_values(ascending=False).reset_index()
plt.figure(figsize=(12,6))
sns.barplot(data=avg_salary_by_job, x='COMPRATE', y='JOBTITLE', palette='viridis')
plt.xlabel('Average Annual Salary')
plt.ylabel('Job Title')
plt.title('Average Salary Across Top 10 Job Titles')
plt.grid(axis='x')
plt.tight_layout()
plt.show()
avg_salary_by_job = df_top_jobs.groupby('JOBTITLE')['COMPRATE'].mean().sort_values(ascending=False)
print(avg_salary_by_job)
/var/folders/yk/cc83vdwn3xg3md1lpw0n1wdc0000gn/T/ipykernel_64583/583941898.py:7: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.
JOBTITLE Teacher, Special Ed. 104953.061789 Teacher, General Secondary 104093.381703 Teacher, General Elementary 102316.545380 Program Analyst 97424.158355 OFFICER 91484.685345 FIREFIGHTER EMT 76493.019087 CORRECTIONAL OFFICER 71271.113039 Motor Vehicle Operator 478.137587 Bus Attendant 24.790784 SUBSTITUTE TEACHER 22.449592 Name: COMPRATE, dtype: float64
By examining the average salaries of the ten most common job titles in the D.C. public workforce, a clear hierarchy of occupational stratification emerges. Certified professional roles such as Special Education Teacher, Secondary School Teacher, and Program Analyst all have average annual salaries exceeding $97,000. These positions typically come with stringent entry requirements—such as teaching certifications, civil service examinations, or advanced degrees and technical expertise required for analytical roles. These qualifications increase the perceived "scarcity" of such jobs and make it easier for workers to access higher job classifications and salary grades (such as higher GRADE levels), thereby automatically securing greater opportunities for advancement and income protection within the system.
In stark contrast, positions like Bus Attendant and Substitute Teacher have average annual salaries of only about 24 dollars. It reports unusually low annual salaries, rather than indicating a data entry error, these figures likely reflect the nature of short-term or hourly employment. In these positions, individuals may have only worked for a few days or weeks within the year. Due to the extreme nature of these values, they can not be included in the main analysis of salary comparisons. However, their presence is still analytically significant: they point to a category of work that is often unstable, short-term, and likely not covered by standard contractual protections.
These roles are generally considered “supportive” or “service-based” labor, such as managing student transportation or covering temporary classroom absences. They tend to have low entry barriers and often lack stable employment status or long-term contract protections, making it difficult for workers in these roles to access standard promotion pathways or performance-based raises. Furthermore, although these jobs are essential to the daily functioning of public institutions, they are systematically devalued within institutional pay structures—leading to a persistent disconnect between the actual value of the work and the compensation it receives.
The next step in this analysis focuses on the relationship between gender structure and wage inequality. Although the dataset does not explicitly include employee gender, we can apply a method of “gender-coding” job titles to examine which positions are more traditionally associated with women or men in societal norms, and compare the salary distributions across these categories.
female_coded_jobs = ['Nurse', 'Teacher', 'Clerk', 'Assistant', 'Secretary', 'Social Worker']
male_coded_jobs = ['Police Officer', 'Firefighter', 'Engineer', 'Inspector', 'Supervisor', 'Investigator']
def classify_job(job):
if any(female_job in job for female_job in female_coded_jobs):
return 'Female-coded'
elif any(male_job in job for male_job in male_coded_jobs):
return 'Male-coded'
else:
return 'Other'
df['gender_coding'] = df['JOBTITLE'].apply(classify_job)
df_gender_jobs = df[df['gender_coding'].isin(['Female-coded', 'Male-coded'])]
plt.figure(figsize=(10,6))
sns.boxplot(data=df_gender_jobs, x='gender_coding', y='COMPRATE')
plt.xlabel('Gender-coded Job Category')
plt.ylabel('Annual Salary')
plt.title('Salary Distribution Between Male-coded and Female-coded Positions')
plt.grid(True)
plt.tight_layout()
plt.show()
avg_salary_by_gender_code = df_gender_jobs.groupby('gender_coding')['COMPRATE'].mean()
print(avg_salary_by_gender_code)
gender_coding Female-coded 101126.369828 Male-coded 108986.102324 Name: COMPRATE, dtype: float64
Although traditional "male-coded" occupations are commonly associated with higher pay due to prevailing social stereotypes, this chart presents an apparently counterintuitive yet structurally consistent pattern: the median salary for female-coded positions is sliahtly higher than that of male-coded ones.
Importantly, these gender codes refer not to the actual gender of employees, but to the social perception of certain job roles as “masculine” or “feminine”—a heuristic based on long-standing occupational stereotypes. We have to note that this analytical approach has some biases, but it can still be regarded as reference comparative data for analysis.
The salary distribution for female-coded jobs is more concentrated, with a relatively high median but a constrained upper bound, lacking extreme high-salary outliers. In contrast, male-coded jobs show much greater variability: their median salary is slightly lower, but a subset of high-paying positions significantly elevates the average, suggesting a sharper internal hierarchy. This indicates that while male-coded categories include many lower-wage jobs, they also encompass roles with greater promotion potential and negotiation leverage.
On a deeper level, this pattern may reflect a dual-track structure within the public employment system: on one track, professional female-coded jobs benefit from standardized protections that ensure stable compensation; on the other, male-coded jobs are more likely to fall into broader, more variable salary schemes—shaped by job grade, appointment type, and departmental power—leading to salary extremes without raising the overall median.
To further understand the structural inequalities in salary growth within the D.C. public workforce, we shift our focus from job titles and gender to the evolving dynamics across different government departments. While job titles and gender coding reveal horizontal disparities in pay scales, the departmental dimension offers a lens to observe vertical shifts over time—particularly useful for identifying which departments have received preferential resource allocation over the past decades.
df['HIRE_YEAR'] = pd.to_datetime(df['HIREDATE_STRING'], errors='coerce').dt.year
hiring_trend = df['HIRE_YEAR'].value_counts().sort_index().reset_index()
hiring_trend.columns = ['Year', 'Number of New Hires']
hiring_trend = hiring_trend[hiring_trend['Year'].between(1980, 2025)]
plt.figure(figsize=(12, 5))
plt.plot(hiring_trend['Year'], hiring_trend['Number of New Hires'],
marker='o', linestyle='-', color='teal')
plt.xlabel('Year')
plt.ylabel('Number of New Hires')
plt.title('Hiring Trend Over Time (1980–2025)')
plt.grid(True)
plt.tight_layout()
plt.show()
df['DESCRSHORT'] = df['DESCRSHORT'].str.title()
dept_counts = df['DESCRSHORT'].value_counts().head(15).reset_index()
dept_counts.columns = ['Department', 'Number of Employees']
plt.figure(figsize=(12, 6))
bars = plt.bar(dept_counts['Department'], dept_counts['Number of Employees'],
color='slateblue', edgecolor='black')
plt.xticks(rotation=45, ha='right')
plt.ylabel('Number of Employees')
plt.title('Top 15 Departments by Number of Employees')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
From these two graphs, we can figure out the question: which departments grow the most—and under what conditions? The hiring trend chart illustrates a long-term upward trajectory in government employment, with sharp increases after 2015 and a dramatic spike around 2021, likely tied to pandemic-related staffing demands. But growth alone is not evenly distributed. The department size chart shows that a small number of agencies—especially DCPS (public schools), MPD (police), and FEMS (fire and emergency services)—employ disproportionately large shares of the workforce. These departments not only dominate headcount but are also likely to benefit from consistent budgetary support.
Meanwhile, other essential but less visible services—such as sanitation, transportation, and public housing—rarely appear among the top employers. This discrepancy reflects a deeper structural reality: headcount growth is not simply a response to labor need but also a function of political visibility.
In this light, structural inequality in the public sector is not just about who gets paid more—but also about who gets hired, retained, and consistently resourced. Departments at the intersection of public visibility and political value continue to expand, while others remain chronically under-supported, even as they carry the operational backbone of the city.
Behind the Data¶
Data Feminism (2020) points out: “what’s missing is as important as what’s present.” In the case of the D.C. public employee dataset, we are not given information about race, gender identity, part-time hours, or union representation—all of which profoundly affect labor conditions. Such absences in data are rarely neutral; rather, they often reflect institutional choices. In this case, the decision not to record race may function as a form of depoliticization—rendering racialized inequalities invisible at the data level and, by extension, absent from policy discourse. As D’Ignazio and Klein argue (2020), “Power is not evenly distributed.” Those with greater institutional visibility and voice are more likely to be represented in official records, while marginalized groups may lack even the right to be seen.
Therefore, when analyzing structural inequality within the public employment system, we must recognize that we are able to see—salary, employment type, job title, and so on—are merely “cooked data (data that has been selected, processed, and standardized)”(D’Ignazio & Klein, 2020) that have been filtered and formatted through institutional processes. Although these data appear standardized and easy to analyze, they do not represent an objective or complete picture; rather, they reflect the portion of reality that the government has chosen to present through its data governance practices. For example, the scope of data collection, the methods of classification, and the standards for disclosure are all shaped and regulated by underlying power structures. In other words, the “government data” we analyze is itself a tool of governance. It renders those who are institutionally excluded from data collection less visible in public discourse, policy evaluation, and even budget allocation. Within the public employment system, this selective visibility—constructed through institutional choices—forms the first barrier to addressing inequality.
Conclusion¶
Structural inequality is not the result of individual choices, but of institutional design and regimes of visibility (Hurrell, 2005). The disparity between regular and temporary employees’ salaries, along with the significantly faster salary growth observed in certain government departments, points to the existence of a “hidden hierarchy” within the public sector. This phenomenon is not merely the outcome of individual effort, but rather a reflection of how institutional resources are allocated and how roles are structurally positioned. While the data suggests that female-coded occupations have a slightly higher median salary than male-coded ones, this does not imply that women hold a systemic advantage within the civil service. More likely, it reflects the standardized protections attached to specific roles, rather than genuine gender equity.
At the same time, what appears to be “neutral” data often embodies institutional choices. The absence of variables such as race not only limits the depth of our analysis but also contributes to the systematic erasure of certain forms of inequality at the data level. This makes the experiences of some people "invisible" and also quietly conceals certain inequalities.
This study is not without its limitations. The classification of gender-coded occupations may lack full precision, and questions may arise regarding the continuity and completeness of the dataset itself. Nonetheless, long-term public salary records of this nature remain rare within the realm of open government data. Overall, this dataset offers a valuable entry point for understanding how inequality is both constructed and sustained within the architecture of the civil service.
References¶
D’Ignazio, C., & Klein, L. (2020). 4. “What Gets Counted Counts.” In Data Feminism. https://data-feminism.mitpress.mit.edu/pub/h1w0nbqp
D’Ignazio, C., & Klein, L. (2020). 6. The Numbers Don’t Speak for Themselves. In Data Feminism. Retrieved from https://data-feminism.mitpress.mit.edu/pub/czq9dfs5
Hurrell, A. (2005). Power, institutions, and the production of inequality. Power in global governance, 56.
Kinnard, M. (2025, February 19). Fired federal workers hunt for new jobs but struggle to replace their ... Associated Press. https://apnews.com/article/doge-firings-layoffs-federal-government-workers-musk-57671a76f22d5398a3c794e29e350082